Strategic Bidding in Transmission Constrained Electricity Markets Using Artificial Bee Colony Algorithm

This article presents a methodology for a supplier's optimal bidding strategy considering network constraints and rivals' bidding behaviors. This has been formulated as a bi-level multi objective optimization problem, in which the lower-level problem represents the market clearing process by the system operator for minimization of customers' payments. The upper-level problem is a multi-objective formulation that simultaneously considers profit maximization and risk minimization. Pareto-optimal solutions of the bi-level multi-objective optimization problem have been obtained using the ε-constraint method. A fuzzy decision-making process has been used to derive the most efficient solution among the Pareto-optimal solutions. Upper-level objective functions are highly non-linear; therefore, the artificial bee colony algorithm, a relatively new heuristic approach, has been applied to obtain the best solution to the bi-level multi objective optimization problem for a day-ahead hourly market. The effectiveness of the proposed method has been established on a 2-bus system and the IEEE-30 bus test system. The results, obtained using the artificial bee colony algorithm, have been compared with those obtained using a genetic algorithm based approach.